在线测量水中的COD和酸盐对抗随机背景干扰,基于紫外可见光谱和基于物理的多任务学习
Jiacheng Liu1, Tao Yu2, Xueji Wang2
1Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China; University of Chinese Academy of Sciences, Beijing 100049, China; Department of Mechanical Engineering, National University of Singapore, 117575, Singapore.
概括
一个新的基于物理的多任务学习模型改善了水质监测. 它准确地测量化学氧气需求 (COD) 和酸盐水平,即使有来自度和颜色的背景干扰.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 由于光谱重叠和背景干扰,传统的UV-Vis光谱学难以同时测量COD和酸盐.
- 水中的度和色度会引起随机背景噪声,阻碍准确的长期分析.
研究的目的:
- 开发一种新的UV-Vis光谱定量分析模型,用于准确的在线水质监测.
- 解决在具有挑战性的水条件下测量化学氧需求 (COD) 和酸盐现有方法的局限性.
主要方法:
- 引入一个紧的双光学路径频谱检测传感器.
- 设计一个基于物理的多任务学习 (PI-MTL) 模型,整合物理知识.
- 实施多任务损失包裹策略,以适应随机背景.
主要成果:
- 该PI-MTL模型实现了高预测R2值:COD为0.941和酸盐为0.9575.
- 与PLSR相比,根的平方平均误差 (RMSE) 显著减少:COD为60.89%,酸盐为77.3%.
- 超过了基准CNN模型的表现,降低了COD的RMSE30.59%,酸盐的65.96%.
结论:
- PI-MTL模型在线多参数水质监测方面表现出卓越的性能.
- 这种方法可以长期精确测量COD和酸盐,克服随机背景干扰.
- 开发的光谱传感器和模型显示了环境监测应用的巨大潜力.
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